A. Field of the Invention
The present invention relates to methods and apparatus for reducing distortions in sampled communication signals received over transmission media, and more particularly relates to error filtering in a hybrid equalizer system for generating an accurate estimate of the original transmitted signal.
B. Description of the Related Art
Linear and decision feedback equalizers are commonly used in communication receivers for computer systems providing applications such as multimedia, modem communications, optical and magnetic storage devices, and wireless systems. In high-speed modem data communication systems, it is often advantageous to incorporate an adaptive decision-feedback equalizer (hereafter referred to as DFE) in conjunction with an adaptive linear equalizer (hereafter referred to as LE). For computational efficiency, the least-mean-squares (LMS) algorithm is often used as a method for adapting the coefficients of the equalizers.
LE and DFE structures have been studied in detail for their simple structures and adequate performance over well-behaved channels. The DFE has enjoyed some success as a form of equalizer for data communication receivers that attempt to eliminate intersymbol interference (ISI), due to the DFE's simplicity and its ability, unlike the LE, to perform well over channels having spectral nulls, while advantageously providing a structure without noise enhancement. However, the fundamental problem with a LE/DFE system is that, for impairments that can be mitigated by either the LE or the DFE, deep convergence of the joint system is very slow, sometimes on the order of millions of symbol periods.
Equalizers may operate in a continuously adaptive mode to track variations in the communications channel. Such continuously adaptive modes, however, typically require a significant computational burden. A method of alleviating this computational overhead in a continuous adaptive mode is to adapt the equalizer coefficients periodically based upon a known training sequence. However, channel mismatch still occurs due to problems with periodic training and the like, and an inability to be optimized for considerable channel fluctuations. Computer simulations have been found useful for predicting channel fluctuations for equalizers, but the computational requirements still may be quite significant. As described in U.S. Pat. No. 4,985,902 to Gurcan for "Decision Feedback Equalizer and a Method of Operating a Decision Feedback Equalizer," issued Jan. 15, 1991, a method attempts to optimize the peak reference tap of a DFE by optimizing the mean square error (MSE) performance of the DFE in the presence of arbitrary channel mismatch.
Gurcan makes the assumption that increasing the number of feed-forward taps improves the MSE performance. U.S. Pat. No. 5,513,214 to Gozzo for "System and Method of Estimating Equalizer Performance in the Presence of Channel Mismatch," issued Apr. 30, 1996, however, indicates that while increasing the number of feed-forward taps may improve performance under ideal conditions, such is not always the case under severe channel mismatch conditions. Gozzo observes that existing performance estimators either found the MSE by ignoring channel mismatch, or calculated the MSE using cumbersome and/or expensive analysis or simulation techniques which are not amiable to real-time applications. Therefore, Gozzo attempts to provide quick estimation of equalizer performance under arbitrary channel mismatch conditions to predict the MSE performance of the LE or DFE structure when training the equalizers, assuming a finite length non-recursive type LE. Lee, "A Fast Computation Algorithm for the Decision Feedback Equalizer" IEEE Transactions on Communications, Vol. 43, No. 11, Nov. 1995, also describes a non-recursive MSE-DFE structure for achieving faster convergence. Such non-recursive LE solutions for estimating of equalizer performance using MSE estimation still require considerable computation capabilities.
Previous solutions to the rate of convergence problem have involved, either directly or by some approximation method, collecting data to determine an autocorrellation matrix, then the inversion of the autocorrellation matrix to extract initial coefficients. In directly inverting the autocorrellation matrix, extreme precision and intense computation are required, which becomes unrealistic for LE/DFE structures of some length in a real-time application. Other methods employ one or more approximations to the matrix inversion which adversely affect initial convergence, often achieving results inferior to simple joint LMS update. It would be desirable, therefore, to provide faster convergence of the LE and DFE coefficients using a recursive adaptation algorithm.